Method for detecting bending strength of fiberboard

Abstract

The invention discloses a method for detecting the bending strength of a fiberboard, and relates to the technical field of mechanical stress tests.The method comprises the steps that a loading mechanism is arranged above the fiberboard; wherein the loading mechanism comprises a loading rod capable of reciprocating along the vertical direction; a loading mechanism is started, so that a loading rod drives a flexible loading head to move downwards to approach and make contact with the fiberboard; after the flexible loading head is in contact with the fiberboard, the loading rod continuously drives the flexible loading head to move downwards according to a preset first nonlinear speed curve; meanwhile, the bending deformation amount of the fiberboard and the pressure distribution situation detected by the multiple pressure sensing units are recorded; when the bending deformation amount of the fiberboard reaches a first preset proportion value of the thickness of the fiberboard, the loading process is paused, and the initial bending strength condition of the fiberboard is determined according to the pressure distribution condition detected by the multiple pressure sensing units; and the bending strength value of the fiberboard is determined according to the initial bending strength condition of the fiberboard.

Description

Technical Field

[0001] The present application relates to the technical field of mechanical stress testing, and in particular to a method for testing the bending strength of fiberboard. Background Art

[0002] Fiberboard is a man-made board made of fiber materials interwoven and formed by their inherent adhesive properties. During the production process, adhesives and / or additives can be added as needed. Its fiber raw materials come from a wide range of sources, including wood fibers, other plant fibers, polyester fibers, recycled fibers, etc. This board has excellent texture, which not only retains the physical and mechanical properties of wood, but also solves the problem of large differences in longitudinal and transverse strength caused by the single arrangement direction of wood fibers, and has strong dimensional stability. The surface of fiberboard is flat and knot-free, and it can be made into large-format boards of different thicknesses and densities. Therefore, it has a wide range of applications and performs well in furniture manufacturing, mattress production, architectural decoration, vehicle and shipbuilding, packaging and other fields.

[0003] Due to the wide range of uses of fiberboard, the stability of its mechanical properties is of great importance. The mechanical properties of fiberboards with different raw materials, manufacturing processes and application fields vary greatly.

[0004] Therefore, there is an urgent need for a fiberboard bending strength testing method to evaluate the bending strength of the fiberboard after the fiberboard is produced, so as to ensure its performance and safety in practical applications. Summary of the invention

[0005] The embodiment of the present application provides a method for testing the bending strength of fiberboard to solve the above-mentioned technical problems.

[0006] The present application provides a method for testing the bending strength of a fiberboard, comprising: placing the fiberboard on a supporting component of a testing device; A loading mechanism is arranged above the fiberboard; wherein the loading mechanism comprises a loading rod capable of reciprocating in a vertical direction; a flexible loading head is connected to the lower end of the loading rod; the flexible loading head is composed of a plurality of retractable airbags, each of which is provided with a pressure sensing unit on its surface, and the pressure sensing unit is used to detect the pressure distribution on the fiberboard in real time; Starting the loading mechanism so that the loading rod drives the flexible loading head to move downward to approach and contact the fiberboard; After the flexible loading head contacts the fiberboard, the loading rod continues to drive the flexible loading head to move downward at a preset first nonlinear speed curve to apply a first pressure to the fiberboard; and simultaneously records the bending deformation of the fiberboard and the pressure distribution detected by a plurality of pressure sensing units; wherein the first nonlinear speed curve is a curve that accelerates first and then decelerates; When the bending deformation of the fiberboard reaches a first preset proportional value of the thickness of the fiberboard, the loading process is suspended, and the preliminary bending strength of the fiberboard is determined according to the pressure distribution detected by the multiple pressure sensing units; The bending strength value of the fiberboard is determined according to the preliminary bending strength condition of the fiberboard.

[0007] Further, the step of determining the bending strength value of the fiberboard according to the preliminary bending strength of the fiberboard comprises: Applying a preset restoring force opposite to the initial anti-bending deformation direction to the fiberboard to restore the fiberboard to an initial state; then applying a second pressure to the fiberboard based on a preset second nonlinear speed curve until the bending deformation of the fiberboard reaches a second preset proportional value of the thickness of the fiberboard; wherein the second nonlinear speed curve is a curve that accelerates first and then decelerates; Repeat the loading process and the recovery process several times; The bending strength value of the fiberboard is determined according to the pressure distribution detected by multiple pressure sensing units during multiple loading processes and the initial bending strength of the fiberboard.

[0008] Furthermore, the detection device further comprises an optical fiber sensor array; the optical fiber sensor array is electrically connected to a data processing unit of the detection device; while the loading mechanism is arranged above the fiberboard, the optical fiber sensor array is arranged below the fiberboard; Using the optical fiber sensor array, real-time monitoring of the internal stress distribution of the fiberboard during multiple loading processes and recovery processes is performed; Determining the bending strength value of the fiberboard according to the pressure distribution detected by the multiple pressure sensing units during the multiple loading processes and the preliminary bending strength of the fiberboard includes: Calculate the bending strength distribution diagram of the fiberboard according to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, the pressure distribution detected by multiple pressure sensing units, and the preliminary bending strength of the fiberboard; The bending strength value of the fiberboard is evaluated according to the bending strength distribution diagram of the fiberboard.

[0009] Furthermore, the calculation of the bending strength distribution diagram of the fiberboard according to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, the pressure distribution detected by multiple pressure sensing units, and the preliminary bending strength of the fiberboard includes: According to the preliminary bending strength of the fiberboard, the characteristics of the multi-layer composite material structure of the fiberboard and the stress characteristics, the stress-strain relationship equation of the fiberboard under the bending load and the deformation coordination conditions of each layer are determined; Performing a superposition correlation analysis on the internal stress distribution monitored by the optical fiber sensor array and the pressure distribution detected by multiple pressure sensing units during multiple loading processes; According to the result of the superposition correlation analysis, the stress-strain relationship equation and the deformation coordination conditions of each layer, a stress-pressure relationship model at multiple positions on the fiberboard is established; Utilizing the stress-pressure relationship model and combining material parameters and geometric parameters of each layer of the fiberboard, a bending strength distribution diagram of the fiberboard is calculated; Among them, the material parameters of each layer of the fiberboard include elastic modulus, Poisson's ratio and density; the geometric parameters of each layer of the fiberboard include the thickness of each layer, fiber arrangement direction and distribution of adhesive.

[0010] Furthermore, the stress-strain relationship equation of the fiberboard under the bending load and the deformation coordination conditions of each layer are determined according to the initial bending strength of the fiberboard, the characteristics of the multi-layer composite material structure of the fiberboard and the stress characteristics, including: A representative volume element model of the fiberboard is generated according to the initial bending strength of the fiberboard, the characteristics of the multilayer composite material structure of the fiberboard and the stress characteristics by using a random sequential adsorption algorithm, including: determining the shape, size and distribution of the fibers according to the initial bending strength of the fiberboard, the characteristics of the multilayer composite material structure of the fiberboard and the stress characteristics, randomly selecting the starting point and direction of the fibers, and gradually depositing the fibers into the representative volume element model until a preset volume fraction is reached; verifying the generated representative volume element model by finite element analysis; wherein the fibers are located at Orientation The probability of deposition is calculated based on the following formula: ; in, Is the fiber in position Orientation Probability of deposition; It's location distance to the nearest deposited fiber; is the characteristic length of fiber deposition, characterizing the randomness of fiber deposition; is the polar angle of the fiber; is the azimuth of the fiber; and represents the target fiber orientation; is the standard deviation of fiber orientation; is a normalization constant used to ensure that the sum of the probability distribution is 1; represents the exponential function; Based on the representative volume element model, using the Monte Carlo algorithm, simulating the mechanical properties under different fiber orientations and distributions to determine the stress-strain relationship equation of the fiberboard under bending load; Based on the representative volume element model and the preliminary bending strength of the fiberboard, the deformation coordination conditions of each layer of the fiberboard are determined using the genetic algorithm-sequential quadratic programming algorithm; wherein the optimization objective function of the genetic algorithm-sequential quadratic programming algorithm is: ; ; in, It is Layer stress; is the target stress; It is Layer strain; It is Layer strain; is the number of layers; is the initial strain; is the regularization parameter, which is used to balance the weight of target stress and initial strain; is the inter-layer coordination weight, which is used to coordinate the deformation between layers.

[0011] Furthermore, the superposition correlation analysis of the internal stress distribution monitored by the optical fiber sensor array and the pressure distribution detected by the multiple pressure sensing units during the multiple loading processes includes: Using a convolutional neural network, according to the internal stress distribution monitored by the optical fiber sensor array and the pressure distribution detected by the plurality of pressure sensing units, a characteristic graph of the internal stress distribution and a characteristic graph of the pressure distribution are determined; Aligning and superimposing the characteristic graph of the internal stress distribution and the characteristic graph of the pressure distribution; Based on the superposition result, combined with the stress-strain relationship equation of the fiberboard and the deformation coordination conditions of each layer, the result of the superposition correlation analysis is determined; Wherein, aligning and superimposing the characteristic graph of the internal stress distribution and the characteristic graph of the pressure distribution comprises: Performing feature fusion on the characteristic map of the internal stress distribution and the characteristic map of the pressure distribution to obtain an initial fusion feature; Extracting local channel attention features and global channel attention features of the initial fusion features through a multi-scale channel attention module; Inputting the local channel attention feature and the global channel attention feature into an iterative attention feature fusion module to perform iterative feature fusion; Among them, the aligned feature map is ; ; in, Indicates that the Minimized feature map ; is a characteristic diagram of the internal stress distribution; is a characteristic diagram of pressure distribution; Representation feature map Characteristic diagram of internal stress distribution The mean square error between Representation feature map Characteristic diagram of pressure distribution The mean square error between Representation feature map loss of attention; is the weight coefficient used to balance the contribution of the characteristic map of internal stress distribution and the characteristic map of pressure distribution; is the attention loss weight.

[0012] Furthermore, the support component comprises two support seats which can move relatively in the horizontal direction, and each support seat is provided with an arc-shaped groove which matches the thickness of the fiberboard; A driving motor is provided at the bottom of the supporting seat, and the driving motor is used to automatically adjust the distance between the two supporting seats according to the length of the fiberboard to be detected and the detection requirements; wherein the driving motor is electrically connected to the control unit of the detection device.

[0013] Furthermore, the loading mechanism also includes an internal environment simulation system; the internal environment simulation system adjusts the temperature, humidity and air pressure during the detection process in real time according to preset environmental parameters to simulate the use of the fiberboard in different environments.

[0014] Furthermore, the airbag of the flexible loading head is filled with a smart gel material with a temperature compensation function, and the smart gel material can automatically adjust its own hardness and elasticity at different temperatures.

[0015] Furthermore, the inner surface of the arc-shaped groove is coated with a layer of nano-lubricating material to reduce the friction between the fiberboard and the support seat.

[0016] Based on the embodiment provided by the present application, a flexible loading head is used, which is composed of a plurality of retractable airbags, and each airbag surface is provided with a pressure sensing unit. This design can detect the pressure distribution on the fiberboard in real time, and can more comprehensively and accurately reflect the stress distribution state of the fiberboard during the force-bearing process compared with the traditional single-point loading method. This accurate pressure distribution detection capability helps to more accurately evaluate the bending strength of the fiberboard and avoid misjudgment caused by local stress concentration. The movement of the loading rod adopts a preset first nonlinear speed curve, that is, a method of accelerating first and then decelerating. This nonlinear loading method can be closer to the dynamic loading situation that the fiberboard may encounter during actual use, and can more truly reflect the changes in the mechanical properties of the fiberboard at different loading stages compared with the traditional uniform speed loading, thereby improving the reliability and practicality of the test results. During the test process, by recording the bending deformation and pressure distribution of the fiberboard, the loading process can be suspended when the bending deformation of the fiberboard reaches the first preset proportional value of its thickness, and the initial bending strength of the fiberboard can be determined according to the pressure distribution. This dynamic evaluation method can judge the bending strength of the fiberboard in advance when it is not completely destroyed, avoid irreversible damage to the fiberboard caused by excessive loading, and also improve the safety and efficiency of the detection. The detection method of the present invention is applicable to fiberboards with different raw materials, manufacturing processes and application fields. Due to the large differences in the mechanical properties of fiberboards, the flexible loading head and the nonlinear loading method can better adapt to the characteristics of different fiberboards, provide accurate bending strength evaluation for various types of fiberboards, and have wide applicability. By real-time recording and analyzing the pressure distribution, combined with the dynamic loading process, the present invention can quickly and accurately determine the bending strength value of the fiberboard. This efficient and accurate detection method can significantly improve the efficiency of fiberboard quality detection, reduce the detection cost, and provide more powerful technical support for the production, application and quality control of fiberboard. The detection method of the present invention can effectively evaluate the performance and safety of fiberboard in actual use. Through accurate bending strength detection, it can ensure that the fiberboard can withstand the expected load in the application of furniture manufacturing, building decoration, vehicle and shipbuilding, etc., avoid safety hazards caused by insufficient strength, thereby enhancing the overall application value and market competitiveness of the fiberboard. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings: Figure 1 is a flow chart of an optional fiberboard bending strength testing method according to an embodiment of the present application; Figure 2It is a flow chart of another optional fiberboard bending strength testing method according to an embodiment of the present application.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Alternatively, if Figure 1 As shown, the present application provides a method for testing the bending strength of a fiberboard, comprising: S101, placing the fiberboard on a supporting component of a detection device; S102, a loading mechanism is arranged above the fiberboard; wherein the loading mechanism comprises a loading rod capable of reciprocating in a vertical direction; a flexible loading head is connected to the lower end of the loading rod; the flexible loading head comprises a plurality of retractable airbags, each of which has a pressure sensing unit on its surface, and the pressure sensing unit is used to detect the pressure distribution on the fiberboard in real time; Wherein, the loading mechanism may be, but is not limited to, arranged above the midpoint of the fiberboard; S103, starting the loading mechanism so that the loading rod drives the flexible loading head to move downward to approach and contact the fiberboard; S104, after the flexible loading head contacts the fiberboard, the loading rod continues to drive the flexible loading head to move downward at a preset first nonlinear speed curve to apply a first pressure to the fiberboard; and simultaneously records the bending deformation of the fiberboard and the pressure distribution detected by the multiple pressure sensing units; wherein the first nonlinear speed curve is a curve that first accelerates and then decelerates; In this embodiment, the acceleration of the acceleration section of the first nonlinear speed curve can be 0.1m / s² to 0.5m / s²; the deceleration of the deceleration section of the first nonlinear speed curve can be 0.2m / s² to 1m / s².

[0021] S105, when the bending deformation of the fiberboard reaches a first preset proportional value of the thickness of the fiberboard, suspending the loading process, and determining the preliminary bending strength of the fiberboard according to the pressure distribution detected by the multiple pressure sensing units; In this embodiment, the first preset ratio value may include but is not limited to 15%-25%.

[0022] S106, determining the bending strength value of the fiberboard according to the preliminary bending strength of the fiberboard.

[0023] Based on the embodiment provided by the present application, a flexible loading head is used, which is composed of a plurality of retractable airbags, and each airbag surface is provided with a pressure sensing unit. This design can detect the pressure distribution on the fiberboard in real time, and can more comprehensively and accurately reflect the stress distribution state of the fiberboard during the force-bearing process compared with the traditional single-point loading method. This accurate pressure distribution detection capability helps to more accurately evaluate the bending strength of the fiberboard and avoid misjudgment caused by local stress concentration. The movement of the loading rod adopts a preset first nonlinear speed curve, that is, a method of accelerating first and then decelerating. This nonlinear loading method can be closer to the dynamic loading situation that the fiberboard may encounter during actual use, and can more truly reflect the changes in the mechanical properties of the fiberboard at different loading stages compared with the traditional uniform speed loading, thereby improving the reliability and practicality of the test results. During the test process, by recording the bending deformation and pressure distribution of the fiberboard, the loading process can be suspended when the bending deformation of the fiberboard reaches the first preset proportional value of its thickness, and the initial bending strength of the fiberboard can be determined according to the pressure distribution. This dynamic evaluation method can judge the bending strength of the fiberboard in advance when it is not completely destroyed, avoid irreversible damage to the fiberboard caused by excessive loading, and also improve the safety and efficiency of the detection. The detection method of the present invention is applicable to fiberboards with different raw materials, manufacturing processes and application fields. Due to the large differences in the mechanical properties of fiberboards, the flexible loading head and the nonlinear loading method can better adapt to the characteristics of different fiberboards, provide accurate bending strength evaluation for various types of fiberboards, and have wide applicability. By real-time recording and analyzing the pressure distribution, combined with the dynamic loading process, the present invention can quickly and accurately determine the bending strength value of the fiberboard. This efficient and accurate detection method can significantly improve the efficiency of fiberboard quality detection, reduce the detection cost, and provide more powerful technical support for the production, application and quality control of fiberboard. The detection method of the present invention can effectively evaluate the performance and safety of fiberboard in actual use. Through accurate bending strength detection, it can ensure that the fiberboard can withstand the expected load in the application of furniture manufacturing, building decoration, vehicle and shipbuilding, etc., avoid safety hazards caused by insufficient strength, thereby enhancing the overall application value and market competitiveness of the fiberboard.

[0024] Further, the bending strength value of the fiberboard is determined according to the preliminary bending strength of the fiberboard, including: Applying a preset restoring force opposite to the initial anti-bending deformation direction to the fiberboard to restore the fiberboard to its initial state; then applying a second pressure to the fiberboard based on a preset second nonlinear speed curve until the bending deformation of the fiberboard reaches a second preset proportional value of the thickness of the fiberboard; wherein the second nonlinear speed curve is a curve that first accelerates and then decelerates; In this embodiment, when the fiberboard is subjected to the first pressure, it will bend downward. This bending direction can be regarded as a vector pointing to the concave surface of the fiberboard. For example, if the fiberboard is subjected to downward pressure in the middle, the fiberboard will bend downward to form a concave shape. In order to restore the fiberboard to its initial flat state, a force opposite to the direction of the initial anti-bending deformation needs to be applied. This force is called a restoring force. The direction of the restoring force should be upward, opposite to the bending direction of the fiberboard, to offset the bending deformation and restore the fiberboard to its initial flat state.

[0025] In this embodiment, the acceleration of the acceleration section of the second nonlinear speed curve can be 0.1m / s² to 0.5m / s²; the deceleration of the deceleration section of the second nonlinear speed curve can be 0.2m / s² to 1m / s².

[0026] Repeat the loading process and the recovery process several times; The bending strength value of the fiberboard is determined according to the pressure distribution detected by multiple pressure sensing units during multiple loading processes and the initial bending strength of the fiberboard.

[0027] After each loading-recovery process, the surface cleaning unit of the detection device will automatically clean the surface of the fiberboard to remove dust, impurities and possible tiny fiber debris on the surface to ensure the accuracy of the next loading process. The surface cleaning unit includes an ultrasonic cleaning head and an electrostatic adsorption device. The ultrasonic cleaning head is used to loosen the attachments on the surface of the fiberboard, and the electrostatic adsorption device is used to adsorb the loosened attachments to achieve efficient cleaning of the fiberboard surface.

[0028] Furthermore, the detection device further comprises an optical fiber sensor array; the optical fiber sensor array is electrically connected to the data processing unit of the detection device; while the loading mechanism is arranged above the fiberboard, the optical fiber sensor array is arranged below the fiberboard; The fiber optic sensor array is used to monitor the internal stress distribution of the fiberboard in the multiple loading and recovery processes in real time. The data processing unit calculates the bending strength distribution diagram of the fiberboard through the finite element analysis algorithm based on the monitoring data of the fiber optic sensor array and the pressure value of the pressure sensing unit, providing a more detailed reference for the quality assessment of the fiberboard. Alternatively, if Figure 2As shown, according to the pressure distribution detected by multiple pressure sensing units during multiple loading processes and the initial bending strength of the fiberboard, the bending strength value of the fiberboard is determined, including: S201, calculating a bending strength distribution diagram of the fiberboard according to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, the pressure distribution detected by multiple pressure sensing units, and the preliminary bending strength of the fiberboard; S202, evaluating the bending strength value of the fiberboard according to the bending strength distribution diagram of the fiberboard.

[0029] Furthermore, according to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, combined with the pressure distribution detected by multiple pressure sensing units and the preliminary bending strength of the fiberboard, the bending strength distribution diagram of the fiberboard is calculated, including: According to the initial bending strength of the fiberboard, the characteristics of the multi-layer composite material structure of the fiberboard and the stress characteristics, the mechanical property differences of each layer and the interaction between the layers are considered; based on the classical laminate theory and elastic mechanics theory, the stress-strain relationship equation of the fiberboard under bending load and the deformation coordination conditions of each layer are determined; The internal stress distribution monitored by the fiber optic sensor array and the pressure distribution detected by multiple pressure sensing units during multiple loading processes are analyzed by superposition correlation. When performing superposition correlation analysis, the dynamic relationship between internal stress and pressure distribution under nonlinear velocity curve loading is analyzed. For example, during the acceleration stage of the loading rod, the pressure detected by the pressure sensing unit increases rapidly, and the internal stress monitored by the fiber optic sensor array also increases rapidly accordingly. However, during the deceleration stage, the changes in pressure and internal stress gradually stabilize. According to the results of superposition correlation analysis, stress-strain relationship equation and deformation coordination conditions of each layer, the stress-pressure relationship model at multiple locations on the fiberboard is established; The bending strength distribution diagram of the fiberboard is calculated by using the stress-pressure relationship model and combining the material parameters and geometric parameters of each layer of the fiberboard. Among them, the material parameters of each layer of the fiberboard include elastic modulus, Poisson's ratio and density; the geometric parameters of each layer of the fiberboard include the thickness of each layer, fiber arrangement direction and distribution of adhesive.

[0030] Furthermore, according to the preliminary bending strength of the fiberboard, the characteristics of the multi-layer composite material structure of the fiberboard and the stress characteristics, the stress-strain relationship equation of the fiberboard under the bending load and the deformation coordination conditions of each layer are determined, including: Using the random sequential adsorption algorithm, a representative volume element model of the fiberboard is generated according to the initial bending strength of the fiberboard, the characteristics of the multilayer composite structure of the fiberboard and the stress characteristics, including: According to the initial bending strength of the fiberboard, the characteristics of the multilayer composite material structure of the fiberboard and the stress characteristics, the shape, size and distribution of the fibers are determined, the starting point and direction of the fibers are randomly selected, and the fibers are gradually deposited into the representative volume element model until the preset volume fraction is reached; the generated representative volume element model is verified by finite element analysis to ensure that it can accurately reflect the microstructure and mechanical properties of the fiberboard; the fibers are located at the Orientation The probability of deposition is calculated based on the following formula: ; in, Is the fiber in position Orientation Probability of deposition; It's location distance to the nearest deposited fiber; is the characteristic length of fiber deposition, characterizing the randomness of fiber deposition; is the polar angle of the fiber; is the azimuth of the fiber; and represents the target fiber orientation; is the standard deviation of fiber orientation; is a normalization constant used to ensure that the sum of the probability distribution is 1; represents the exponential function; Among them, the random order adsorption algorithm deposits fibers into the three-dimensional model in a random order until the desired volume fraction is reached; Based on the representative volume element model, the mechanical properties of the fiberboard under different fiber orientations and distributions were simulated using the Monte Carlo algorithm to determine the stress-strain relationship equation of the fiberboard under bending load. Based on the representative volume element model and the preliminary bending strength of the fiberboard, the deformation coordination conditions of each layer of the fiberboard are determined by using the genetic algorithm-sequential quadratic programming algorithm. According to the structural characteristics of the multi-layer composite material of the fiberboard, a finite element model including the material properties and geometric parameters of each layer is constructed. The genetic algorithm-sequential quadratic programming algorithm is used to optimize the multi-layer structure of the fiberboard, and the parameters such as fiber angle and layer thickness are adjusted to improve the mechanical properties and determine the deformation coordination conditions of each layer under bending load. Among them, the optimization objective function of the genetic algorithm-sequential quadratic programming algorithm is: ; ; in, It is Layer stress; is the target stress; It is Layer strain; It is Layer strain; is the number of layers; is the initial strain; is the regularization parameter, which is used to balance the weight of target stress and initial strain; is the inter-layer coordination weight, which is used to coordinate the deformation between layers.

[0031] In this embodiment, the regularization parameter and inter-layer coordination weights The specific value of can be adjusted according to the application scenario and actual needs of the fiberboard. For example: In the aerospace field, fiberboards usually need to have high bending strength and good inter-layer coordination to cope with extreme mechanical environments. In this case: the regularization parameter A larger value, such as 0.5-1.0, can be used to ensure that the target stress matches the actual stress more closely, thereby ensuring the performance of the fiberboard under high-intensity loads. It can be taken as 0.3-0.5 to ensure the deformation coordination between layers and avoid failure modes such as interlayer delamination.

[0032] In the automotive industry, fiberboard needs to maintain a certain strength while taking into account cost and processing efficiency. Therefore, the regularization parameter It can be taken as 0.3-0.5 to balance the weight of target stress and initial strain to ensure the performance of fiberboard under dynamic load. It can be taken as 0.2-0.3 to ensure the coordination of interlayer deformation while avoiding excessive optimization leading to increased costs.

[0033] In construction and bridge engineering, fiberboards are usually required to have good durability and overall stability. It can be taken as 0.2-0.4 to ensure uniform stress distribution of fiberboard in long-term use. It can be taken as 0.1-0.2 to ensure the deformation coordination between layers and avoid local stress concentration.

[0034] For general industrial applications, the performance requirements of fiberboard are relatively low, and smaller parameter values ​​can be used. It can be taken as 0.1-0.3 to simplify the calculation while ensuring basic stress matching. It can be taken below 0.1 to reduce the computational complexity.

[0035] In summary, the regularization parameter The selection range is generally between 0.1 and 1.0, and the specific value depends on the requirements for the matching degree between the target stress and the initial strain. The selection range is generally between 0.1-0.5, and the specific value depends on the requirements for interlayer deformation coordination.

[0036] In practical applications, the parameters can be optimized and adjusted by combining experimental verification and numerical simulation to ensure that the bending strength and overall performance of the fiberboard meet actual needs.

[0037] Furthermore, the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes and the pressure distribution detected by multiple pressure sensing units are superimposed and correlated, including: Using a convolutional neural network, a characteristic map of internal stress distribution and a characteristic map of pressure distribution are determined based on the internal stress distribution monitored by the optical fiber sensor array and the pressure distribution detected by multiple pressure sensing units; Align and overlay the characteristic graph of internal stress distribution and the characteristic graph of pressure distribution; Based on the superposition results, combined with the stress-strain relationship equation of the fiberboard and the deformation coordination conditions of each layer, the results of the superposition correlation analysis are determined; Among them, the characteristic map of internal stress distribution and the characteristic map of pressure distribution are aligned and superimposed, including: Perform feature fusion on the feature map of internal stress distribution and the feature map of pressure distribution to obtain initial fusion features; Through the multi-scale channel attention module, the local channel attention features and the global channel attention features of the initial fusion features are extracted; Input the local channel attention features and the global channel attention features into the iterative attention feature fusion module for iterative feature fusion; Among them, the aligned feature map is ; ; in, Indicates that the Minimized feature map ; is a characteristic diagram of the internal stress distribution; is a characteristic diagram of pressure distribution; Representation feature map Characteristic diagram of internal stress distribution The mean square error between Representation feature map Characteristic diagram of pressure distribution The mean square error between Representation feature map loss of attention; is the weight coefficient used to balance the contribution of the characteristic map of internal stress distribution and the characteristic map of pressure distribution; is the attention loss weight.

[0038] In this embodiment, the weight coefficient and attention loss weight The specific value of can be adjusted according to the application scenario and actual needs of the fiberboard. For example: In the aerospace field, detection accuracy and reliability are extremely high, and it is necessary to ensure the accuracy and stability of feature fusion. It can be set to 0.7-0.9 to pay more attention to the contribution of the internal stress distribution feature map and ensure the accuracy of the stress distribution. It can be taken as 0.1-0.3 to appropriately increase the weight of attention loss and ensure the robustness of feature fusion.

[0039] In the automotive industry, detection efficiency and cost control are more important, and a balance needs to be struck between accuracy and efficiency. It can be set to 0.5-0.7 to balance the contribution of internal stress distribution and pressure distribution. It can be taken as 0.05-0.1 to appropriately reduce the weight of attention loss to reduce computational complexity.

[0040] In building and bridge engineering, the coverage and integrity of detection are more critical, and it is necessary to ensure the comprehensiveness of feature fusion. It can be set to 0.3-0.5 to pay more attention to the contribution of the pressure distribution feature map. It can be taken as 0.01-0.05 to reduce the weight of attention loss and ensure the extensiveness of feature fusion.

[0041] In general industrial applications, the versatility and adaptability of detection are more important, and the parameters can be relatively loose. It can be set to 0.2-0.4 to simplify the calculation. Attention loss weight It can be set below 0.01 to further reduce the computational complexity.

[0042] In summary, the weight coefficient The value range is generally between 0.2-0.9, and the specific value depends on the importance attached to the internal stress distribution and pressure distribution characteristic diagram. The value range is generally between 0.01-0.3, and the specific value depends on the weight requirement for attention loss.

[0043] In practical applications, the parameters can be optimized and adjusted by combining experimental verification and numerical simulation to ensure the accuracy and reliability of fiberboard bending strength testing.

[0044] In this embodiment, feature alignment: use image processing technology (such as interpolation, registration, etc.) to align the feature map of internal stress distribution and the feature map of pressure distribution to the same size and spatial position to ensure the spatial consistency of the two. Initial feature fusion: perform initial feature fusion on the aligned feature map of internal stress distribution and the feature map of pressure distribution to obtain initial fusion features. Multi-scale channel attention module: extract local channel attention features and global channel attention features of the initial fusion features through the multi-scale channel attention module.

[0045] Among them, the multi-scale channel attention module can contain two branches, one for extracting local channel attention and the other for extracting global channel attention. The specific implementation can refer to the following steps: Local channel attention: Use a small convolution kernel (such as 3x3) to extract local features, and then use the channel attention mechanism (such as SE module) to extract local channel attention features. Global channel attention: Use a large convolution kernel (such as 7x7) or global average pooling to extract global features, and then use the channel attention mechanism to extract global channel attention features.

[0046] Iterative attention feature fusion module: The local channel attention features and the global channel attention features are input into the iterative attention feature fusion module for iterative feature fusion.

[0047] Among them, the iterative attention feature fusion module gradually optimizes the feature fusion results by alternately integrating the initial feature fusion and the attention module.

[0048] Furthermore, the supporting component includes two supporting seats that can move relatively in the horizontal direction, and each supporting seat is provided with an arc-shaped groove that matches the thickness of the fiberboard; A driving motor is provided at the bottom of the support seat, and the driving motor is used to automatically adjust the distance between the two support seats according to the length of the fiberboard to be tested and the testing requirements, so that the fiberboard can be stably placed on the support seat; wherein, the driving motor is electrically connected to the control unit of the detection device.

[0049] Furthermore, the loading mechanism also includes an internal environment simulation system; the internal environment simulation system adjusts the temperature, humidity and air pressure during the detection process in real time according to preset environmental parameters to simulate the use of the fiberboard in different environments, thereby more comprehensively and accurately evaluating the bending strength of the fiberboard.

[0050] In this embodiment, the temperature adjustment range is -20°C-60°C, the humidity adjustment range is 10%-90%RH, and the air pressure adjustment range is 0.05MPa-0.2MPa.

[0051] Furthermore, the airbag of the flexible loading head is filled with a smart gel material with temperature compensation function. The smart gel material can automatically adjust its own hardness and elasticity at different temperatures to ensure that the pressure applied by the loading head to the fiberboard is uniform and stable at different ambient temperatures.

[0052] Furthermore, the inner surface of the arc-shaped groove is coated with a layer of nano-lubricating material to reduce the friction between the fiberboard and the support seat.

[0053] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for testing the bending strength of fiberboard, characterized in that: include: placing the fiberboard on the support member of the testing device; A loading mechanism is arranged above the fiberboard; wherein the loading mechanism comprises a loading rod capable of reciprocating in a vertical direction; a flexible loading head is connected to the lower end of the loading rod; the flexible loading head is composed of a plurality of retractable airbags, each of which is provided with a pressure sensing unit on its surface, and the pressure sensing unit is used to detect the pressure distribution on the fiberboard in real time; Starting the loading mechanism so that the loading rod drives the flexible loading head to move downward to approach and contact the fiberboard; After the flexible loading head contacts the fiberboard, the loading rod continues to drive the flexible loading head to move downward at a preset first nonlinear speed curve to apply a first pressure to the fiberboard; and simultaneously records the bending deformation of the fiberboard and the pressure distribution detected by a plurality of pressure sensing units; wherein the first nonlinear speed curve is a curve that accelerates first and then decelerates; When the bending deformation of the fiberboard reaches a first preset proportional value of the thickness of the fiberboard, the loading process is suspended, and the preliminary bending strength of the fiberboard is determined according to the pressure distribution detected by the multiple pressure sensing units; The bending strength value of the fiberboard is determined according to the preliminary bending strength condition of the fiberboard.

2. The fiberboard bending strength testing method according to claim 1, characterized in that: Determining the bending strength value of the fiberboard according to the preliminary bending strength of the fiberboard includes: Applying a preset restoring force opposite to the initial anti-bending deformation direction to the fiberboard to restore the fiberboard to an initial state; then applying a second pressure to the fiberboard based on a preset second nonlinear speed curve until the bending deformation of the fiberboard reaches a second preset proportional value of the thickness of the fiberboard; wherein the second nonlinear speed curve is a curve that accelerates first and then decelerates; Repeat the loading process and the recovery process several times; The bending strength value of the fiberboard is determined according to the pressure distribution detected by multiple pressure sensing units during multiple loading processes and the initial bending strength of the fiberboard.

3. The fiberboard bending strength testing method according to claim 2, characterized in that: The detection device further comprises an optical fiber sensor array; the optical fiber sensor array is electrically connected to a data processing unit of the detection device; while the loading mechanism is arranged above the fiberboard, the optical fiber sensor array is arranged below the fiberboard; Using the optical fiber sensor array, real-time monitoring of the internal stress distribution of the fiberboard during multiple loading processes and recovery processes is performed; Determining the bending strength value of the fiberboard according to the pressure distribution detected by the multiple pressure sensing units during the multiple loading processes and the preliminary bending strength of the fiberboard includes: Calculate the bending strength distribution diagram of the fiberboard according to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, the pressure distribution detected by multiple pressure sensing units, and the preliminary bending strength of the fiberboard; The bending strength value of the fiberboard is evaluated according to the bending strength distribution diagram of the fiberboard.

4. The fiberboard bending strength testing method according to claim 3, characterized in that: The method of calculating the bending strength distribution diagram of the fiberboard according to the internal stress distribution monitored by the optical fiber sensor array during multiple loading processes, the pressure distribution detected by multiple pressure sensing units, and the preliminary bending strength of the fiberboard includes: According to the preliminary bending strength of the fiberboard, the characteristics of the multi-layer composite material structure of the fiberboard and the stress characteristics, the stress-strain relationship equation of the fiberboard under the bending load and the deformation coordination conditions of each layer are determined; Performing a superposition correlation analysis on the internal stress distribution monitored by the optical fiber sensor array and the pressure distribution detected by multiple pressure sensing units during multiple loading processes; According to the result of the superposition correlation analysis, the stress-strain relationship equation and the deformation coordination conditions of each layer, a stress-pressure relationship model at multiple positions on the fiberboard is established; Utilizing the stress-pressure relationship model and combining material parameters and geometric parameters of each layer of the fiberboard, a bending strength distribution diagram of the fiberboard is calculated; Among them, the material parameters of each layer of the fiberboard include elastic modulus, Poisson's ratio and density; the geometric parameters of each layer of the fiberboard include the thickness of each layer, fiber arrangement direction and distribution of adhesive.

5. The fiberboard bending strength testing method according to claim 4, characterized in that: The stress-strain relationship equation of the fiberboard under the bending load and the deformation coordination conditions of each layer are determined according to the preliminary bending strength of the fiberboard, the characteristics of the multi-layer composite material structure of the fiberboard and the stress characteristics, including: A representative volume element model of the fiberboard is generated according to the initial bending strength of the fiberboard, the characteristics of the multilayer composite material structure of the fiberboard and the stress characteristics by using a random sequential adsorption algorithm, including: determining the shape, size and distribution of the fibers according to the initial bending strength of the fiberboard, the characteristics of the multilayer composite material structure of the fiberboard and the stress characteristics, randomly selecting the starting point and direction of the fibers, and gradually depositing the fibers into the representative volume element model until a preset volume fraction is reached; verifying the generated representative volume element model by finite element analysis; wherein the fibers are located at Orientation The probability of deposition is calculated based on the following formula: ； in, Is the fiber in position Orientation Probability of deposition; It's location distance to the nearest deposited fiber; is the characteristic length of fiber deposition, characterizing the randomness of fiber deposition; is the polar angle of the fiber; is the azimuth of the fiber; and represents the target fiber orientation; is the standard deviation of fiber orientation; is a normalization constant used to ensure that the sum of the probability distribution is 1; represents the exponential function; Based on the representative volume element model, using a Monte Carlo algorithm, simulating the mechanical properties under different fiber orientations and distributions to determine the stress-strain relationship equation of the fiberboard under bending load; Based on the representative volume element model and the preliminary bending strength of the fiberboard, the deformation coordination conditions of each layer of the fiberboard are determined using the genetic algorithm-sequential quadratic programming algorithm; wherein the optimization objective function of the genetic algorithm-sequential quadratic programming algorithm is: ; ； in, It is Layer stress; is the target stress; It is Layer strain; It is Layer strain; is the number of layers; is the initial strain; is the regularization parameter, which is used to balance the weight of target stress and initial strain; is the inter-layer coordination weight, which is used to coordinate the deformation between layers.

6. The fiberboard bending strength testing method according to claim 4, characterized in that: The superposition correlation analysis of the internal stress distribution monitored by the optical fiber sensor array and the pressure distribution detected by multiple pressure sensing units during multiple loading processes includes: Using a convolutional neural network, according to the internal stress distribution monitored by the optical fiber sensor array and the pressure distribution detected by the plurality of pressure sensing units, a characteristic graph of the internal stress distribution and a characteristic graph of the pressure distribution are determined; Aligning and superimposing the characteristic graph of the internal stress distribution and the characteristic graph of the pressure distribution; Based on the superposition result, combined with the stress-strain relationship equation of the fiberboard and the deformation coordination conditions of each layer, the result of the superposition correlation analysis is determined; Wherein, aligning and superimposing the characteristic graph of the internal stress distribution and the characteristic graph of the pressure distribution comprises: Performing feature fusion on the characteristic map of the internal stress distribution and the characteristic map of the pressure distribution to obtain an initial fusion feature; Extracting local channel attention features and global channel attention features of the initial fusion features through a multi-scale channel attention module; Inputting the local channel attention feature and the global channel attention feature into an iterative attention feature fusion module to perform iterative feature fusion; Among them, the aligned feature map is ; ； in, Indicates that the Minimized feature map ; is a characteristic diagram of the internal stress distribution; is a characteristic diagram of pressure distribution; Representation feature map Characteristic diagram of internal stress distribution The mean square error between Representation feature map Characteristic diagram of pressure distribution The mean square error between Representation feature map loss of attention; is the weight coefficient used to balance the contribution of the characteristic map of internal stress distribution and the characteristic map of pressure distribution; is the attention loss weight.

7. The fiberboard bending strength testing method according to claim 1, characterized in that: The support component comprises two support seats which can move relative to each other in the horizontal direction, and each support seat is provided with an arc-shaped groove which matches the thickness of the fiberboard; A driving motor is provided at the bottom of the supporting seat, and the driving motor is used to automatically adjust the distance between the two supporting seats according to the length of the fiberboard to be detected and the detection requirements; wherein the driving motor is electrically connected to the control unit of the detection device.

8. The fiberboard bending strength testing method according to claim 1, characterized in that: The loading mechanism also includes an internal environment simulation system; the internal environment simulation system adjusts the temperature, humidity and air pressure during the detection process in real time according to preset environmental parameters to simulate the use of the fiberboard in different environments.

9. The fiberboard bending strength testing method according to claim 1, characterized in that: The air bag of the flexible loading head is filled with a smart gel material with a temperature compensation function, and the smart gel material can automatically adjust its own hardness and elasticity at different temperatures.

10. The fiberboard bending strength testing method according to claim 7, characterized in that: The inner surface of the arc-shaped groove is coated with a layer of nano-lubricating material to reduce the friction between the fiberboard and the support seat.

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